Power inspection UAV task assignment matrix reversal genetic algorithm

Abstract

Traditional manual power inspections are characterized by low efficiency, lengthy processes, and high costs. Existing research on UAV-based power inspections has often overlooked critical factors such as the risk levels of target tasks, the duration of tasks executed by UAVs, and the utility per unit task. To address these gaps, this paper proposes a task allocation method for UAV power inspections based on the Time Window Matrix Reversal Genetic Algorithm (TMGA). Firstly, the proposed cost model accounts for the risk levels of inspection tasks and the impact of low-altitude flight on energy consumption. Secondly, an inspection task allocation model is constructed with the goal of maximizing UAV inspection unit utility. The model is then optimized using two-point crossover and single-point reversal mutation operations, which enhance the UAV unit utility and generate an optimal allocation matrix. The performance of TMGA is evaluated through simulation experiments in three different scenarios, comparing it with existing algorithms. The results show that TMGA outperforms these algorithms in terms of average task time, task completion rate, and unit utility. Specifically, TMGA reduces the average task time by 37% compared to the Cluster Grouping Consensus-base Bundle Algorithm and improves task unit utility by 56.91% compared to the Genetic Algorithm.